ABSTRACT
Over a decade, as a specialized workshop in the field of text mining applied to biomedical informatics, DTMBIO (ACM international workshop on Data and Text Mining in Biomedical Informatics) has been held annually in conjunction with one of the largest data management conferences, CIKM. The purpose of DTMBIO is to foster discussions regarding the state-of-the-art applications of data and text mining on biomedical research problems. To address our purpose, we bring together researchers working on computer science and bio/medical informatics area including text mining and high throughput genomic data analysis, such as the next generation Sequencing (NGS) data. DTMBIO 2020 will help scientists navigate emerging trends and opportunities in the evolving area of informatics related techniques and problems in the context of biomedical research.
Supplemental Material
- Harshit Jain, Nishant Raj and Suyash Mishra. Deep User A Sui Generis QA Approach using RoBERTa for Adverse Drug Event Identification. Under review in DTMBIO 2020.Google Scholar
- Thomas Brox Røst, Laura Slaughter, Øystein Nytrø, Ashley Elizabeth Muller and Gunn Elisabeth Vist. Using Deep Learning to Support High-Quality Evidence Mapping. Under review in DTMBIO 2020.Google Scholar
- Hyojung Paik, Jinhwa Jang, Inseok Ko and Jong-Yeup Kim. Identification of the Association between Alzheimer's Diseases and Viral Infection using Digital Healthcare Records of Millions of Patients in South Korea. Under review in DTMBIO 2020.Google Scholar
- Oh-kyoung Kwon, Yongseong Cho and Hyojung Paik. Supercomputer Aided Acceleration of Longitudinal Genome-Wide Association Study. Under review in DTMBIO 2020.Google Scholar
- Keyuan Jiang, Liyuan Huang, Tingyu Chen, Gelareh Karbaschi, Dingkai Zhang and Gordon Bernard. Mining Potentially Unreported Effects from Twitter Posts through Relational Similarity: A Case for Opioids. Under review in DTMBIO 2020.Google Scholar
- Sangjae Seo, Jung Woo Park, Dosik An, Junwon Yoon and Soonwook Hwang. Supercomputer-aided Drug Repositioning at Scale: Virtual Screening for SARS-CoV-2 Protease Inhibitor. Under review in DTMBIO 2020.Google Scholar
- Sung-Gwon Lee, Dokyun Na and Chungoo Park. Comparability of reference-based and reference-free transcriptome analysis approaches at the gene expression level. Under review in DTMBIO2020.Google Scholar
- Jooseong Oh, Sung-Gwon Lee and Chungoo Park. PIC-Me: Paralogs and Isoforms Classifier based on Machine-learning approaches. Under review in DTMBIO2020.Google Scholar
- Rahul Mehta, Yang Lu and Muge Karaman. Mapping of Lesion Images to Somatic Mutations. Under review in DTMBIO2020.Google Scholar
- Minji Lee, Seonwoo Jung, Sejin Bae, Eunjoo Lee, Yeon-Yong Kim, Doheon Lee, Minkeun Song and Sunyong Yoo. Machine learning-based prediction of stroke risk in atrial fibrillation using Korean national health insurance data. Under review in DTMBIO2020.Google Scholar
- Jingyu Lee, Myeong-Sang Yu and Dokyun Na. Computational prediction of drug-induced liver injury of drug candidates. Under review in DTMBIO2020.Google Scholar
- Kwangmin Kim and Doheon Lee. Context Aware Multi-token Concept Recognition of Biological Entities. Under review in DTMBIO2020.Google Scholar
- Judita Preiss. Literature Based Discovery: Using Deep Learning for Candidate Selection. Under review in DTMBIO2020.Google Scholar
Index Terms
DTMBIO 2020: The Fourteenth International Workshop on Data and Text Mining in Biomedical Informatics
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